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Base editing sensor libraries for high-throughput engineering and functional analysis of cancer-associated single
Francisco J Sánchez-Rivera1,2,3, Bianca J Diaz4,5, Edward R Kastenhuber1,4
1Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Nature Biotechnology
|February 15, 2022
Summary
This study introduces novel base-editing sensors to efficiently test thousands of single guide RNA (sgRNA) and base editor combinations. This resource aids in characterizing cancer-associated variants and developing new cancer models.
Area of Science:
- Molecular Biology
- Genetics
- Cancer Research
Background:
- Base editing is crucial for characterizing single nucleotide variants of unknown function.
- Identifying optimal single guide RNA (sgRNA) and base editor combinations is a significant challenge.
Purpose of the Study:
- To develop and utilize modular base-editing-activity sensors to systematically measure editing efficiency and precision.
- To create a comprehensive resource of sgRNAs for interrogating cancer-associated single nucleotide variants.
- To streamline the production of in vivo cancer models and aid in interpreting high-throughput base editing screens.
Main Methods:
- Development of modular base-editing-activity sensors linking sgRNAs and target sites.
- Systematic measurement of editing efficiency and precision across >200,000 editor-sgRNA combinations.
- Integration of sensor modules into pooled sgRNA libraries for high-throughput screening.
Main Results:
- Quantification of editing across a vast number of editor-sgRNA combinations, creating a valuable resource.
- Demonstration that sensor-validated tools accelerate the creation of in vivo cancer models.
- Identification of previously uncharacterized mutant TP53 alleles driving cancer cell proliferation and tumor development.
Conclusions:
- The developed sensor framework facilitates the functional interrogation of cancer variants in various model systems.
- This approach enhances the interpretation of base editing screens and aids in the development of novel cancer therapies.
- The study provides a scalable method for variant characterization and functional genomics in cancer research.
